Collaborative filtering technology and cluster model technology have created processes that are used in the furtherance of business for generating sets of items that a consumer might find appealing. Many of these processes start by finding a set of customers that have purchased and rated items. The process chooses items that have interacted with similar customers, eliminates items according to business rules and presents a rank-ordered list of the remaining items to the end user.
Using collaborative filtering technology to generate items of interest or recommendations can become computationally very expensive when the data sets become large. In an effort to reduce the cost of computation, the data set is generally restricted by reducing the number of users considered, either arbitrarily or by heuristic mechanisms or by restricting the number of items. The wholesale reduction of the data set may negatively affect the quality of the items or content to be recommended. In addition, most correlation systems require the maintenance of a series of very large matrices—this can be computationally costly.
Therefore what has been needed and heretofore unavailable is a system and method for the dynamic generation of correlation scores between arbitrary objects to create a list of correlated items.